import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt 
import seaborn as sns
import warnings

warnings.filterwarnings('ignore')

Presenting Signs of Burnout

data=pd.read_csv('train.csv')
data.head(2)

	Employee ID	Date of Joining	Gender	Company Type	WFH Setup Available	Designation	Resource Allocation	Mental Fatigue Score	Burn Rate
0	fffe32003000360033003200	9/30/2008	Female	Service	No	2	3.0	3.8	0.16
1	fffe3700360033003500	11/30/2008	Male	Service	Yes	1	2.0	5.0	0.36

data.shape

(22750, 9)

Data Understanding

data.describe()

	Designation	Resource Allocation	Mental Fatigue Score	Burn Rate
count	22750.000000	21369.000000	20633.000000	21626.000000
mean	2.178725	4.481398	5.728188	0.452005
std	1.135145	2.047211	1.920839	0.198226
min	0.000000	1.000000	0.000000	0.000000
25%	1.000000	3.000000	4.600000	0.310000
50%	2.000000	4.000000	5.900000	0.450000
75%	3.000000	6.000000	7.100000	0.590000
max	5.000000	10.000000	10.000000	1.000000

data.dropna(subset=['Burn Rate'],inplace=True)

data.isna().sum()

Employee ID                0
Date of Joining            0
Gender                     0
Company Type               0
WFH Setup Available        0
Designation                0
Resource Allocation     1278
Mental Fatigue Score    1945
Burn Rate                  0
dtype: int64

data.shape

(21626, 9)

data.dtypes

Employee ID              object
Date of Joining          object
Gender                   object
Company Type             object
WFH Setup Available      object
Designation               int64
Resource Allocation     float64
Mental Fatigue Score    float64
Burn Rate               float64
dtype: object

Burn Rate

sns.histplot(data,x=data['Burn Rate'])

<AxesSubplot:xlabel='Burn Rate', ylabel='Count'>

sns.boxplot(x=data['Burn Rate'])

<AxesSubplot:xlabel='Burn Rate'>

data['Burn Rate']

0        0.16
1        0.36
2        0.49
3        0.20
4        0.52
         ... 
22745    0.41
22746    0.59
22747    0.72
22748    0.52
22749    0.61
Name: Burn Rate, Length: 21626, dtype: float64

data[(data['Resource Allocation'].notna()) | (data['Mental Fatigue Score'].notna())]

	Employee ID	Date of Joining	Gender	Company Type	WFH Setup Available	Designation	Resource Allocation	Mental Fatigue Score	Burn Rate
0	fffe32003000360033003200	9/30/2008	Female	Service	No	2	3.0	3.8	0.16
1	fffe3700360033003500	11/30/2008	Male	Service	Yes	1	2.0	5.0	0.36
2	fffe31003300320037003900	3/10/2008	Female	Product	Yes	2	NaN	5.8	0.49
3	fffe32003400380032003900	11/3/2008	Male	Service	Yes	1	1.0	2.6	0.20
4	fffe31003900340031003600	7/24/2008	Female	Service	No	3	7.0	6.9	0.52
...	...	...	...	...	...	...	...	...	...
22745	fffe31003500370039003100	12/30/2008	Female	Service	No	1	3.0	NaN	0.41
22746	fffe33003000350031003800	1/19/2008	Female	Product	Yes	3	6.0	6.7	0.59
22747	fffe390032003000	11/5/2008	Male	Service	Yes	3	7.0	NaN	0.72
22748	fffe33003300320036003900	1/10/2008	Female	Service	No	2	5.0	5.9	0.52
22749	fffe3400350031003800	1/6/2008	Male	Product	No	3	6.0	7.8	0.61

21439 rows × 9 columns

data=data[(data['Resource Allocation'].notna()) | (data['Mental Fatigue Score'].notna())]

Empoyee ID

sns.scatterplot(data['Employee ID'].str.slice(16,18),data['Burn Rate'])
#no pattern hidden in employee ID

<AxesSubplot:xlabel='Employee ID', ylabel='Burn Rate'>

Gender

sns.FacetGrid(data,col='Gender',aspect=2).map(plt.hist,'Burn Rate',color='lightblue',bins=10)

<seaborn.axisgrid.FacetGrid at 0x7f435d7ef1f0>

Company Type

sns.FacetGrid(data,col='Company Type',aspect=2).map(plt.hist,'Burn Rate',color='lightblue',bins=10)

<seaborn.axisgrid.FacetGrid at 0x7f435d5eda90>

WFH Setup Available

sns.FacetGrid(data,col='WFH Setup Available',aspect=2).map(plt.hist,'Burn Rate',color='lightblue',bins=40)

<seaborn.axisgrid.FacetGrid at 0x7f435d573df0>

Date of Joining

data['Date of Joining'].nunique()

366

data['Date of Joining']=pd.to_datetime(data['Date of Joining'],format='%m/%d/%Y')

Day=data['Date of Joining'].map(lambda x:x.day)
Month=data['Date of Joining'].map(lambda x:x.month)
Year=data['Date of Joining'].map(lambda x:x.year)

Year.unique()
# All the data belong to 2008, therefore this feature is not useful

array([2008])

data.insert(1,'Day',Day)
data.insert(2,'Month',Month)
data.head(2)

	Employee ID	Day	Month	Date of Joining	Gender	Company Type	WFH Setup Available	Designation	Resource Allocation	Mental Fatigue Score	Burn Rate
0	fffe32003000360033003200	30	9	2008-09-30	Female	Service	No	2	3.0	3.8	0.16
1	fffe3700360033003500	30	11	2008-11-30	Male	Service	Yes	1	2.0	5.0	0.36

sns.catplot(data=data, x='Month', y='Burn Rate', kind='box')

<seaborn.axisgrid.FacetGrid at 0x7f435d749e80>

sns.catplot(data=data, x='Day', y='Burn Rate', kind='box')

<seaborn.axisgrid.FacetGrid at 0x7f435d8ab070>

Designation

data['Designation'].unique()

array([2, 1, 3, 0, 4, 5])

sns.FacetGrid(data,col='Designation',aspect=2,col_wrap=3).map(plt.hist,'Burn Rate',color='lightblue',bins=10)# Should consider as categorical,(will try as continous first though)

<seaborn.axisgrid.FacetGrid at 0x7f435d351550>

sns.catplot(data=data, x='Designation', y='Burn Rate', kind='box')
#looks like we found another jackpot, should use as categorical for sure

<seaborn.axisgrid.FacetGrid at 0x7f435d11b4c0>

# data['Designation']=data['Designation'].astype(str)

Resource Allocation

data['Resource Allocation'].nunique()
# for nan 0 seems suitable number

10

sns.histplot(x=data['Resource Allocation'])

<AxesSubplot:xlabel='Resource Allocation', ylabel='Count'>

sns.catplot(data=data, x='Resource Allocation', y='Burn Rate', kind='box')
#should consider using as categorical Feature

<seaborn.axisgrid.FacetGrid at 0x7f435cc7d070>

# data['Resource Allocation'].fillna(5.0,inplace=True)
# data['Resource Allocation']=data['Resource Allocation'].astype(str)

Mental Fatigue Score

data['Mental Fatigue Score'].nunique()

101

sns.scatterplot(x=data['Mental Fatigue Score'],y=data['Burn Rate'])
# seems like we found the jackpot

<AxesSubplot:xlabel='Mental Fatigue Score', ylabel='Burn Rate'>

Data Preparation

data.dtypes

Employee ID                     object
Day                              int64
Month                            int64
Date of Joining         datetime64[ns]
Gender                          object
Company Type                    object
WFH Setup Available             object
Designation                      int64
Resource Allocation            float64
Mental Fatigue Score           float64
Burn Rate                      float64
dtype: object

train=data.drop(['Employee ID','Date of Joining','Day','Month'],axis=1)
#train['Resource Allocation']=train['Resource Allocation'].astype(str)

train.head()

	Gender	Company Type	WFH Setup Available	Designation	Resource Allocation	Mental Fatigue Score	Burn Rate
0	Female	Service	No	2	3.0	3.8	0.16
1	Male	Service	Yes	1	2.0	5.0	0.36
2	Female	Product	Yes	2	NaN	5.8	0.49
3	Male	Service	Yes	1	1.0	2.6	0.20
4	Female	Service	No	3	7.0	6.9	0.52

train=pd.get_dummies(train,drop_first=True)

cols=train.columns
cols

Index(['Designation', 'Resource Allocation', 'Mental Fatigue Score',
       'Burn Rate', 'Gender_Male', 'Company Type_Service',
       'WFH Setup Available_Yes'],
      dtype='object')

Iterative imputation

from sklearn.experimental import enable_iterative_imputer
from sklearn.impute import IterativeImputer
from sklearn.ensemble import ExtraTreesRegressor,RandomForestRegressor
from catboost import CatBoostRegressor

imp=IterativeImputer(estimator=CatBoostRegressor(),random_state=200)

temp=imp.fit_transform(train)

Learning rate set to 0.067353
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train=pd.DataFrame(temp[:,[train.columns.get_loc(i) for i in cols]], columns=cols)

train['Resource Allocation']=np.round(train['Resource Allocation']).astype(int)

train.isna().sum()

Designation                0
Resource Allocation        0
Mental Fatigue Score       0
Burn Rate                  0
Gender_Male                0
Company Type_Service       0
WFH Setup Available_Yes    0
dtype: int64

train['Resource Allocation'].unique()

array([ 3,  2,  4,  1,  7,  6,  5,  8, 10,  9])

sns.scatterplot(x=train['Mental Fatigue Score'],y=train['Burn Rate'])
# seems like we found the jackpot

<AxesSubplot:xlabel='Mental Fatigue Score', ylabel='Burn Rate'>

sns.catplot(data=data, x='Resource Allocation', y='Burn Rate', kind='box')

<seaborn.axisgrid.FacetGrid at 0x7f435c9ac100>

train.dtypes

Designation                float64
Resource Allocation          int64
Mental Fatigue Score       float64
Burn Rate                  float64
Gender_Male                float64
Company Type_Service       float64
WFH Setup Available_Yes    float64
dtype: object

Resource Allocation Imputation

Feature Reduction and Selection

# train=exp

# train['Resource Allocation']=train['Resource Allocation'].astype(str)

# train=train.drop(['Day'],axis=1)

# train.insert(1,'Month',exp["Month"].to_numpy())

# train['Month']=train['Month'].astype(str)

# train=train.drop(['Month'],axis=1)

train=pd.get_dummies(train,drop_first=True)

corrMat=train.corr()
corrFeat=corrMat.index
plt.figure(figsize=(10,10))
sns.heatmap(corrMat,annot=True,cmap='RdYlGn',linewidths=1.2)

<AxesSubplot:>

#High correlation with Burn Rate is great news we should keep Designation and Mental Fatigue rate as they are strong Predictor

Model Building

Y=train['Burn Rate']
X=train.drop(['Burn Rate'],axis=1)

X.shape

(21439, 6)

X.head()

	Designation	Resource Allocation	Mental Fatigue Score	Gender_Male	Company Type_Service	WFH Setup Available_Yes
0	2.0	3	3.8	0.0	1.0	0.0
1	1.0	2	5.0	1.0	1.0	1.0
2	2.0	4	5.8	0.0	0.0	1.0
3	1.0	1	2.6	1.0	1.0	1.0
4	3.0	7	6.9	0.0	1.0	0.0

from sklearn.model_selection import train_test_split as tts
X_train,X_test,Y_train,Y_test=tts(X,Y,test_size=0.2,random_state=200)

Decision tree dot plot to indentify key drivers

from sklearn.tree import DecisionTreeRegressor
dt_mod=DecisionTreeRegressor(criterion='mse',random_state=200,splitter="random")

dt_mod.fit(X_train,Y_train)
dt_mod.score(X_test,Y_test)

0.912306581821661

dt_mod.feature_importances_

array([0.36022697, 0.2631869 , 0.36430525, 0.00404103, 0.00337908,
       0.00486077])

# import pydotplus as pdt
# import os
# from sklearn import tree
# dot_data=tree.export_graphviz(dt_mod, filled=True, rounded=True, special_characters=True,
#                              proportion=True,feature_names=X.columns)
# graph=pdt.graph_from_dot_data(dot_data)
# from IPython.display import Image
# Image(graph.create_png())

key_drivers=pd.Series(dt_mod.feature_importances_,index=X.columns).sort_values(ascending=False, kind="heapsort")
fig, axes = plt.subplots(1,1,figsize=(12,8))
sns.barplot(x=key_drivers.values,y=key_drivers.index, palette='spring',ax=axes)
fig.savefig("keydrivers.png")

Bagging Ensemble

from xgboost import XGBRegressor
from sklearn.ensemble import BaggingRegressor
from sklearn.tree import DecisionTreeRegressor

# for i in range(1200,1300,30):
#     bagMod=BaggingRegressor(oob_score=True,n_jobs=-1,n_estimators=i,max_samples=1.0,
#                       random_state=200,base_estimator=DecisionTreeRegressor())
#     bagMod.fit(X_train,Y_train)
#     print(f"for {i} estimators accuracy is {bagMod.oob_score_}")

bagMod=BaggingRegressor(oob_score=True,n_jobs=-1,n_estimators=200,max_samples=1.0,
                      random_state=200,base_estimator=DecisionTreeRegressor())

bagMod.fit(X,Y)

BaggingRegressor(base_estimator=DecisionTreeRegressor(), n_estimators=200,
                 n_jobs=-1, oob_score=True, random_state=200)

bagMod.oob_score_

0.9228275842239341

bagMod.score(X_test,Y_test)

0.9516576188838395

from sklearn.linear_model import LinearRegression

lm1=LinearRegression(n_jobs=-1,normalize=True)
lm1.fit(X_train,Y_train)

LinearRegression(n_jobs=-1, normalize=True)

lm1.score(X_test,Y_test)

0.9243868385821757

XGBRegressor

params={
 "learning_rate"    : [0.03] ,
 "max_depth"        : [ 3, 4, 5, 6, 8],
 "min_child_weight" : [ 1, 3, 5, 7,9 ],
 "gamma"            : [ 0.0, 0.1, 0.2 , 0.3,0.4]
}

X_train.dtypes

Designation                float64
Resource Allocation          int64
Mental Fatigue Score       float64
Gender_Male                float64
Company Type_Service       float64
WFH Setup Available_Yes    float64
dtype: object

X_train.shape

(17151, 6)

from sklearn.model_selection import RandomizedSearchCV, GridSearchCV
xgBoost=XGBRegressor()
# random_search=RandomizedSearchCV(xgBoost,param_distributions=params,n_iter=8,cv=6,verbose=3)
grid_search=GridSearchCV(xgBoost,param_grid=params,cv=10)
grid_search.fit(X_train,Y_train)

GridSearchCV(cv=10,
             estimator=XGBRegressor(base_score=None, booster=None,
                                    colsample_bylevel=None,
                                    colsample_bynode=None,
                                    colsample_bytree=None, gamma=None,
                                    gpu_id=None, importance_type='gain',
                                    interaction_constraints=None,
                                    learning_rate=None, max_delta_step=None,
                                    max_depth=None, min_child_weight=None,
                                    missing=nan, monotone_constraints=None,
                                    n_estimators=100, n_jobs=None,
                                    num_parallel_tree=None, random_state=None,
                                    reg_alpha=None, reg_lambda=None,
                                    scale_pos_weight=None, subsample=None,
                                    tree_method=None, validate_parameters=None,
                                    verbosity=None),
             param_grid={'gamma': [0.0, 0.1, 0.2, 0.3, 0.4],
                         'learning_rate': [0.03], 'max_depth': [3, 4, 5, 6, 8],
                         'min_child_weight': [1, 3, 5, 7, 9]})

grid_search.best_estimator_

XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,
             colsample_bynode=1, colsample_bytree=1, gamma=0.0, gpu_id=-1,
             importance_type='gain', interaction_constraints='',
             learning_rate=0.03, max_delta_step=0, max_depth=8,
             min_child_weight=3, missing=nan, monotone_constraints='()',
             n_estimators=100, n_jobs=8, num_parallel_tree=1, random_state=0,
             reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,
             tree_method='exact', validate_parameters=1, verbosity=None)

grid_search.best_params_

{'gamma': 0.0, 'learning_rate': 0.03, 'max_depth': 8, 'min_child_weight': 3}

xgBoost=XGBRegressor(gamma=0.0,learning_rate=0.03,max_depth=8,min_child_weight=1)

xgBoost.fit(X,Y)

XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,
             colsample_bynode=1, colsample_bytree=1, gamma=0.0, gpu_id=-1,
             importance_type='gain', interaction_constraints='',
             learning_rate=0.03, max_delta_step=0, max_depth=8,
             min_child_weight=1, missing=nan, monotone_constraints='()',
             n_estimators=100, n_jobs=8, num_parallel_tree=1, random_state=0,
             reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,
             tree_method='exact', validate_parameters=1, verbosity=None)

xgBoost.score(X_test,Y_test)

0.9348217374218041

# random_search.best_estimator_

# xg=XGBRegressor(base_score=0.5, booster='gbtree',n_estimators=500,n_jobs=-1,max_depth=4,learning_rate=0.03)

# xg.fit(X_train,Y_train)

# xg.score(X_test,Y_test)

Test Data Preparation

test=pd.read_csv('test.csv')

test.dtypes

Employee ID              object
Date of Joining          object
Gender                   object
Company Type             object
WFH Setup Available      object
Designation             float64
Resource Allocation     float64
Mental Fatigue Score    float64
dtype: object

test.isna().sum()

Employee ID             0
Date of Joining         0
Gender                  0
Company Type            0
WFH Setup Available     0
Designation             0
Resource Allocation     0
Mental Fatigue Score    0
dtype: int64

test['Resource Allocation'].nunique()

10

# test['Resource Allocation']=test['Resource Allocation'].astype(int).astype(str)
# test['Designation']=test['Designation'].astype(int).astype(str)

# test.to_csv('TestID.csv',index=False)

testX=test.drop(['Employee ID','Date of Joining'],axis=1)

testX.dtypes

Gender                   object
Company Type             object
WFH Setup Available      object
Designation             float64
Resource Allocation     float64
Mental Fatigue Score    float64
dtype: object

testX.head(1)

	Gender	Company Type	WFH Setup Available	Designation	Resource Allocation	Mental Fatigue Score
0	Female	Service	No	2.0	5.0	7.7

# testX=pd.get_dummies(testX,drop_first=True)

testX.shape

(12250, 6)

testX.head(2)

	Gender	Company Type	WFH Setup Available	Designation	Resource Allocation	Mental Fatigue Score
0	Female	Service	No	2.0	5.0	7.7
1	Female	Product	Yes	1.0	2.0	5.2

# X.to_csv('TestFilter.csv',index=False)

# train= pd.DataFrame(scaler.fit_transform(train), columns = train.columns)
testX.head(1)

	Gender	Company Type	WFH Setup Available	Designation	Resource Allocation	Mental Fatigue Score
0	Female	Service	No	2.0	5.0	7.7

XGBOOST

testX.dtypes

Gender                   object
Company Type             object
WFH Setup Available      object
Designation             float64
Resource Allocation     float64
Mental Fatigue Score    float64
dtype: object

dummy=pd.get_dummies(testX,drop_first=True)

dummy.dtypes

Designation                float64
Resource Allocation        float64
Mental Fatigue Score       float64
Gender_Male                  uint8
Company Type_Service         uint8
WFH Setup Available_Yes      uint8
dtype: object

dummy.shape

(12250, 6)

preds=xgBoost.predict(dummy)

result=pd.DataFrame(data={'Employee ID':test['Employee ID'],'Burn Rate':preds})

result.to_csv('xgBoost.csv',index=False)

Bagging

# preds=random_search.predict(X)

# preds

# result=pd.DataFrame(data={'Employee ID':test['Employee ID'],'Burn Rate':preds})

# result.to_csv('xgBoost.csv',index=False)

bagMod.score(X_test,Y_test)

0.9516576188838395

preds=bagMod.predict(pd.get_dummies(testX))

result=pd.DataFrame(data={'Employee ID':test['Employee ID'],'Burn Rate':preds})

result.to_csv('bag.csv',index=False)

Linear Regression

preds=lm1.predict(dummy)
result=pd.DataFrame(data={'Employee ID':test['Employee ID'],'Burn Rate':preds})
result.to_csv('lm.csv',index=False)

Experimental

CatBoost

testX['Designation']=testX['Designation'].astype(int)
testX['Resource Allocation']=testX['Resource Allocation'].astype(int)
testX['Mental Fatigue Score']=testX['Mental Fatigue Score'].astype(int)
# testX=pd.get_dummies(testX)

testX.dtypes

Gender                  object
Company Type            object
WFH Setup Available     object
Designation              int64
Resource Allocation      int64
Mental Fatigue Score     int64
dtype: object

exp=data.drop(['Employee ID','Date of Joining','Day','Month'],axis=1)

exp['Mental Fatigue Score']=train['Mental Fatigue Score'].to_numpy()
exp['Resource Allocation']=train['Resource Allocation'].to_numpy().astype(int)
train['Mental Fatigue Score']=train['Mental Fatigue Score'].astype(int)

# data=data.dropna(subset=['Burn Rate'])
exp.isna().sum()

Gender                  0
Company Type            0
WFH Setup Available     0
Designation             0
Resource Allocation     0
Mental Fatigue Score    0
Burn Rate               0
dtype: int64

Feature Scaling

trainX=exp.drop(['Burn Rate'],axis=1)
trainY=exp['Burn Rate']
# trainX=train.drop(['Burn Rate'],axis=1)
# trainY=train['Burn Rate']

# trainX=pd.get_dummies(trainX)

# train.isna().sum()

trainX['Mental Fatigue Score']=trainX['Mental Fatigue Score'].astype(int)
trainX.dtypes

Gender                  object
Company Type            object
WFH Setup Available     object
Designation              int64
Resource Allocation      int64
Mental Fatigue Score     int64
dtype: object

# trainX=trainX.drop(['Month'],axis=1)

cat_features=[0,1,2,3,4]

from sklearn.model_selection import train_test_split as tts
X_train, X_test, Y_train, Y_test = tts(trainX, trainY, test_size=0.2, random_state=0)

X_train.dtypes

Gender                  object
Company Type            object
WFH Setup Available     object
Designation              int64
Resource Allocation      int64
Mental Fatigue Score     int64
dtype: object

from catboost import CatBoostRegressor
from sklearn.model_selection import RandomizedSearchCV, GridSearchCV

# model = CatBoostRegressor()

# grid = {'learning_rate': [0.03,0.05],
#         'l2_leaf_reg': [1, 3, 5, 7, 9, 11]}

# grid_search_result = model.randomized_search(grid,cv=20,n_iter=20 ,
#                                        X=trainX, 
#                                        y=trainY, 
#                                        plot=True)

# clf=CatBoostRegressor(depth=10,l2_leaf_reg=7,learning_rate=0.03,iterations=500)

clf = CatBoostRegressor(iterations=1800, learning_rate=0.05,random_seed=200,l2_leaf_reg=13)#1800 best,leaf 13

# for i in range(1000,3000,100):
#     clf = CatBoostRegressor(iterations=i, learning_rate=0.03,random_seed=200,l2_leaf_reg=30)#1800 best,leaf 13
#     clf.fit(X_train,Y_train, cat_features=cat_features,verbose=False)
    
#     print(f"for {i} iterations test score is{clf.score(X_test, Y_test)}")
# # clf.fit(trainX,trainY,verbose=True)
# # clf.fit(trainX,trainY,verbose=True)

# print('CatBoost model is fitted: ' + str(clf.is_fitted()))
# print('CatBoost model parameters:')
# print(clf.get_params())

clf.fit(X_train,Y_train,cat_features=cat_features)

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<catboost.core.CatBoostRegressor at 0x7f439af46550>

clf.score(X_test, Y_test)

0.9195764007156246

# testX=pd.get_dummies(testX)
# testX=scaler.transform(testX)
testX.dtypes

Gender                  object
Company Type            object
WFH Setup Available     object
Designation              int64
Resource Allocation      int64
Mental Fatigue Score     int64
dtype: object

testX['Mental Fatigue Score']=testX['Mental Fatigue Score'].astype(int)
preds=clf.predict(testX)

result=pd.DataFrame(data={'Employee ID':test['Employee ID'],'Burn Rate':preds})

result.to_csv('catboost.csv',index=False)

import dill
dill.dump_session('notebook_env.db')

dill.load_session('notebook_env.db')

Stacking

from sklearn.ensemble import StackingRegressor

X_train, X_test, Y_train, Y_test = tts(pd.get_dummies(trainX), trainY, test_size=0.2, random_state=0)

estimators=[('bagging',BaggingRegressor(oob_score=True,n_jobs=-1,n_estimators=1600,random_state=200,
            base_estimator=DecisionTreeRegressor()))
            ,('xgboost',XGBRegressor(n_estimator=900,n_jobs=-1,learning_rate=0.05,random_state=200)),
            ('catboost',CatBoostRegressor(iterations=1500, learning_rate=0.03,random_seed=200,l2_leaf_reg=13))]

reg=StackingRegressor(estimators=estimators,final_estimator=RandomForestRegressor(n_estimators=600,
                                                                                  oob_score=True,random_state=200))

reg.fit(X_train,Y_train)

[09:11:13] WARNING: ../src/learner.cc:573: 
Parameters: { "n_estimator" } might not be used.

  This may not be accurate due to some parameters are only used in language bindings but
  passed down to XGBoost core.  Or some parameters are not used but slip through this
  verification. Please open an issue if you find above cases.


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[09:12:00] WARNING: ../src/learner.cc:573: 
Parameters: { "n_estimator" } might not be used.

  This may not be accurate due to some parameters are only used in language bindings but
  passed down to XGBoost core.  Or some parameters are not used but slip through this
  verification. Please open an issue if you find above cases.


[09:12:00] WARNING: ../src/learner.cc:573: 
Parameters: { "n_estimator" } might not be used.

  This may not be accurate due to some parameters are only used in language bindings but
  passed down to XGBoost core.  Or some parameters are not used but slip through this
  verification. Please open an issue if you find above cases.


[09:12:01] WARNING: ../src/learner.cc:573: 
Parameters: { "n_estimator" } might not be used.

  This may not be accurate due to some parameters are only used in language bindings but
  passed down to XGBoost core.  Or some parameters are not used but slip through this
  verification. Please open an issue if you find above cases.


[09:12:01] WARNING: ../src/learner.cc:573: 
Parameters: { "n_estimator" } might not be used.

  This may not be accurate due to some parameters are only used in language bindings but
  passed down to XGBoost core.  Or some parameters are not used but slip through this
  verification. Please open an issue if you find above cases.


[09:12:02] WARNING: ../src/learner.cc:573: 
Parameters: { "n_estimator" } might not be used.

  This may not be accurate due to some parameters are only used in language bindings but
  passed down to XGBoost core.  Or some parameters are not used but slip through this
  verification. Please open an issue if you find above cases.


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StackingRegressor(estimators=[('bagging',
                               BaggingRegressor(base_estimator=DecisionTreeRegressor(),
                                                n_estimators=1600, n_jobs=-1,
                                                oob_score=True,
                                                random_state=200)),
                              ('xgboost',
                               XGBRegressor(base_score=None, booster=None,
                                            colsample_bylevel=None,
                                            colsample_bynode=None,
                                            colsample_bytree=None, gamma=None,
                                            gpu_id=None, importance_type='gain',
                                            interaction_constraints=N...
                                            n_estimator=900, n_estimators=100,
                                            n_jobs=-1, num_parallel_tree=None,
                                            random_state=200, reg_alpha=None,
                                            reg_lambda=None,
                                            scale_pos_weight=None,
                                            subsample=None, tree_method=None,
                                            validate_parameters=None,
                                            verbosity=None)),
                              ('catboost',
                               <catboost.core.CatBoostRegressor object at 0x7f4377a6ea90>)],
                  final_estimator=RandomForestRegressor(n_estimators=600,
                                                        oob_score=True,
                                                        random_state=200))

reg.score(X_test,Y_test)

0.9148900896305339

preds=reg.predict(pd.get_dummies(testX))

result=pd.DataFrame(data={'Employee ID':test['Employee ID'],'Burn Rate':preds})

result.to_csv('stack.csv',index=False)